517 citations as recorded by crossref.

1. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al.
2. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al.
3. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al.
4. PARAMETER REGIONALIZATION BASED ON LAND-USE TYPES AND SOIL TYPES FOR A DISTRIBUTED RAINFALL-RUNOFF MODEL D. KATO et al.
5. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al.
6. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al.
7. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al.
8. Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions F. Nogueira Filho et al.
9. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan
10. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al.
11. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al.
12. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling W. Tsai et al.
13. From empirical to physical constraints: Revisiting the structure of monthly water balance models with global evaluation Z. Ning et al.
14. The Anatomy of Uncertain Terrains: Soil Topography Characterization and Discharge Analysis of the Baroro River Basin, Northern Philippines J. Trio et al.
15. Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization D. Jarajapu et al.
16. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al.
17. A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al.
18. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al.
19. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al.
20. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al.
21. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al.
22. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al.
23. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi
24. Gaussian random field-based downscaling for drought projections: linking CMIP6 SSPs to local hydrology in a subtropical river basin S. He et al.
25. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al.
26. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al.
27. Comment on Williams (2025): “Friends don't let friends use NSE or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice” M. Clark et al.
28. Synergizing machine learning and modified physical models for hydrology modeling: A case study of modified SIMHYD and TANK models X. Lei et al.
29. Key drivers of physics-embedded machine learning performance for streamflow prediction A. Adombi & R. Chesnaux
30. Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale G. Abeshu et al.
31. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al.
32. 无资料流域日径流预测<bold>:</bold> 模型迁移优化及参考流域决策分析 昕. 宋 et al.
33. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al.
34. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al.
35. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al.
36. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al.
37. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al.
38. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al.
39. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al.
40. Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al.
41. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al.
42. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
43. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al.
44. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
45. Rising temperatures intensify drought propagation and severity across the contiguous United States H. Yan et al.
46. MACH: A Multi-Attribute Catchment Hydrometeorological dataset K. Sink & T. Brikowski
47. A watershed-scale rainfall infiltration model incorporating water and energy constraints B. Yan & J. Chang
48. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al.
49. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al.
50. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al.
51. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
52. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al.
53. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al.
54. Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins W. Ouyang et al.
55. Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins X. Song et al.
56. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis
57. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al.
58. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al.
59. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al.
60. Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections K. Fowler et al.
61. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al.
62. Runoff for Russia (RFR v1.0): The Large-Sample Dataset of Simulated Runoff and Its Characteristics G. Ayzel
63. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al.
64. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al.
65. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al.
66. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al.
67. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al.
68. Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation V. Andréassian et al.
69. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al.
70. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al.
71. Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
72. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez
73. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al.
74. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al.
75. Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States Y. Wang et al.
76. Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al.
77. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al.
78. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al.
79. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani
80. VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting J. Liu et al.
81. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al.
82. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al.
83. The Coupled Hydrology–Human Activity Information (CHHAI) dataset: a global benchmark dataset for model comparison and evaluation A. Boschee et al.
84. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar
85. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al.
86. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al.
87. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna
88. Catchment processes can amplify the effect of increasing rainfall variability M. Müller et al.
89. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al.
90. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al.
91. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al.
92. Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries D. Thurber et al.
93. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim
94. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al.
95. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al.
96. LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al.
97. Learning landscape features from streamflow with autoencoders A. Bassi et al.
98. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al.
99. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al.
100. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization S. Ragettli et al.
101. HydroQuantum: A new quantum-driven Python package for hydrological simulation M. Saberian et al.
102. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al.
103. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al.
104. A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset M. Mazarei Behbahani & A. Mazarei
105. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al.
106. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al.
107. A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models K. Robles et al.
108. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al.
109. Enhancing large-scale streamflow prediction by coupling structural inductive bias with meta-learning X. Tang et al.
110. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al.
111. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al.
112. Discharge-based classifications of spatio-temporal patterns of potentially gaining and losing subcatchments in the Bode River catchment, Central Germany C. Lei et al.
113. Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records A. Aghababaei et al.
114. Analyzing the Impact of High-Frequency Noise on Hydrological Runoff Modeling: A Frequency-Based Framework for Data Uncertainty Assessment T. Liu et al.
115. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi
116. What is the hydrologically effective area of a catchment? Y. Liu et al.
117. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al.
118. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li
119. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al.
120. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey
121. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al.
122. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al.
123. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta
124. Climate Controls on River Chemistry L. Li et al.
125. A python framework for differentiable hydrological modeling and research workflow automation W. Ouyang et al.
126. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al.
127. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann
128. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell
129. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al.
130. A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo
131. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al.
132. Novel Parameter Calibration Method of WRF-Hydro in Ungauged Areas Combining Satellite-Based Soil Moisture and Multisource Meteorological Data Q. Zhao et al.
133. Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff X. Hu et al.
134. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al.
135. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al.
136. Decoding parameter sensitivities across scales: a Wavelet–Sobol framework for hydrological modelling V. Sai Suswara et al.
137. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al.
138. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi
139. Climate shapes baseflows, influencing drought severity M. Zaerpour et al.
140. A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series H. Zuo et al.
141. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al.
142. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al.
143. Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al.
144. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al.
145. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous
146. Soil Water Accounting Network (SWAN): a novel neural network for modeling conceptual hydrological processes Z. Fang et al.
147. HESS Opinions: Towards a common vision for the future of hydrological observatories P. Nasta et al.
148. Malá zdrojová povodí – jejich prostorové vymezení a klasifikace z hlediska rizika ohrožení rychlým odtokem P. Kavka et al.
149. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al.
150. Seasonal patterns and hydrological regulations of root zone storage capacity across United States S. Du et al.
151. How to deal w___ missing input data M. Gauch et al.
152. Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example Y. Liu et al.
153. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information L. Xu et al.
154. Upland Hillslope Groundwater Subsidy Affects Low‐Flow Storage–Discharge Relationship H. Li & A. Ameli
155. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al.
156. Spatially resolved meteorological and ancillary data in Central Europe for rainfall streamflow modeling M. Vischer et al.
157. Interpretable Budyko-constrained machine learning framework for monthly runoff attribution in U.S. CAMELS basins Z. Yang et al.
158. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al.
159. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo
160. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
161. The Bellinge data set: open data and models for community-wide urban drainage systems research A. Nedergaard Pedersen et al.
162. Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique M. Behbahani et al.
163. Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach M. Brunner & E. Gilleland
164. Regionalizing hydrologic information for runoff predictions beyond continental boundaries using machine learning M. Fathi & A. Awadallah
165. Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms H. Tyralis et al.
166. Deep learning in water protection of resources, environment, and ecology: achievement and challenges X. Fu et al.
167. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan
168. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al.
169. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al.
170. What is the near-natural catchment? An application of hydrological signatures assessment H. Xu et al.
171. Derivation and analysis of spatial variability in master recession curves with mixed effects model B. Chen et al.
172. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions? Y. Khoshkalam et al.
173. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al.
174. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al.
175. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al.
176. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al.
177. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al.
178. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al.
179. Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al.
180. Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI M. Feigl et al.
181. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al.
182. A Deep-Learning based Monthly Precipitation Forecasting with Implications for Urban Design and Water Management M. Behbahani & J. Teymoori
183. Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al.
184. Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration Y. Rong et al.
185. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al.
186. Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5) H. Yan et al.
187. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al.
188. Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping M. Raleigh et al.
189. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli
190. An Aridity Index‐Based Formulation of Streamflow Components A. Meira Neto et al.
191. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al.
192. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al.
193. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al.
194. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler
195. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al.
196. GRHyMoLAP: A process-driven ODE catchment hydrology model inspired by GR4J and HyMoLAP approaches S. Ezéckiel Houénafa et al.
197. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al.
198. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al.
199. Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches J. Wu et al.
200. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis
201. Enhancing Hydro-climatic and land parameter forecasting using Transformer networks S. Subhadarsini et al.
202. Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records M. Vlah et al.
203. Enhancing runoff prediction with causal lag-aware attention and multi-scale fusion in transformer models W. Yuan & H. Yan
204. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al.
205. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al.
206. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma
207. How Probable Is Widespread Flooding in the United States? M. Brunner et al.
208. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al.
209. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al.
210. Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework B. Gordon et al.
211. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al.
212. Using hydrological and climatic catchment clusters to explore drivers of catchment behavior F. Jehn et al.
213. Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates S. Saxe et al.
214. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al.
215. Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models Y. Choi et al.
216. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar
217. Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States K. Khand & G. Senay
218. Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification S. Gharari et al.
219. Improving the Representation of Subsurface Water Movement in Land Models S. Gharari et al.
220. Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al.
221. Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model T. Duong et al.
222. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al.
223. From Rivers to Floodplains: Leveraging Transfer Learning to Predict Floodplain Dissolved Oxygen G. Myers et al.
224. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al.
225. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi
226. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui
227. Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention P. Yang & D. Chen
228. Multi-step regional rainfall-runoff modeling using pyramidal transformer H. Yin et al.
229. Soil CO2 Controls Short‐Term Variation but Climate Regulates Long‐Term Mean of Riverine Inorganic Carbon B. Stewart et al.
230. Improving annual streamflow estimates using Budyko parameterization driven by catchment attributes H. Tamiru et al.
231. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al.
232. Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty
233. What makes a robust calibration period? Insights into the effects of data properties O. Jaffar et al.
234. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method H. Cai et al.
235. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al.
236. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al.
237. Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science J. Janssen et al.
238. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al.
239. Introducing the Model Fidelity Metric (MFM) for robust and diagnostic land surface model evaluation Z. Wu et al.
240. Streamflow shifts with declining snowfall W. Berghuijs & K. Hale
241. AquaFetch: A Unified Python Interface for Water Resource Dataset Acquisition and Harmonization A. Abbas et al.
242. Deep learning for water quality W. Zhi et al.
243. Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting K. Ren et al.
244. Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada’s municipal water supply F. Robinne et al.
245. Another Step Toward “Big” Catchment Science M. Vlah et al.
246. Rainfall-Runoff modeling using transductive long short-term memory and hyperparameter optimization Z. Chen & B. Xie
247. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al.
248. A parsimonious daily water balance model based on the proportionality hypothesis S. Wu et al.
249. Soil drainage modulates climate effects to shape seasonal and mean annual water balances across the southeastern United States Z. Wang et al.
250. Transfer performance of gated recurrent unit model for runoff prediction based on the comprehensive spatiotemporal similarity of catchments X. Min et al.
251. Agriculture’s impact on water–energy balance varies across climates M. Zaerpour et al.
252. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven
253. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
254. Toward routing river water in land surface models with recurrent neural networks M. Lima et al.
255. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al.
256. Catchment Attributes Influencing Performance of Global Streamflow Reanalysis X. Ding
257. A Ranking of Hydrological Signatures Based on Their Predictability in Space N. Addor et al.
258. The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff G. Ayzel et al.
259. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al.
260. Climate change and the opportunity cost of conflict K. Roche et al.
261. An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting W. Fang et al.
262. Deep learning rainfall–runoff predictions of extreme events J. Frame et al.
263. CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking O. Delaigue et al.
264. Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al.
265. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al.
266. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al.
267. Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change H. Li et al.
268. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al.
269. Investigating the effects of local weather, streamflow lag, and global climate information on 1-month-ahead streamflow forecasting by using XGBoost and SHAP: two case studies involving the contiguous USA J. Liu et al.
270. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al.
271. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al.
272. Drivers of Dissolved Organic Carbon Mobilization From Forested Headwater Catchments: A Multi Scaled Approach T. Adler et al.
273. Mechanism Analysis and Demonstration of Effective Information Extraction in the System Differential Response Inversion Estimation Method Y. Chen et al.
274. Can model structure families be inferred from model output? J. Remmers et al.
275. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen
276. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski
277. A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models S. Yoon et al.
278. Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv
279. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy
280. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
281. Streamflow droughts aggravated by human activities despite management A. Van Loon et al.
282. Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al.
283. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al.
284. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al.
285. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al.
286. Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al.
287. Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain R. Lane et al.
288. Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
289. Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does S. Krogh et al.
290. Panta Rhei: a decade of progress in research on change in hydrology and society H. Kreibich et al.
291. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty
292. Improved streamflow simulations in hydrologically diverse basins using physically-informed deep learning models B. Magotra et al.
293. FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications S. Sadeghi Tabas et al.
294. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
295. Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al.
296. Classification of small watersheds in Czechia for the assessment of hydrological response uncertainty T. Janata et al.
297. A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments J. Yang et al.
298. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al.
299. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al.
300. Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study X. Zheng et al.
301. Developing an explainable deep learning module based on the LSTM framework for flood prediction Z. Zhang et al.
302. airGRteaching: an open-source tool for teaching hydrological modeling with R O. Delaigue et al.
303. On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions S. Azimi et al.
304. A Global Benchmark of the Vector-Based Routing Model MizuRoute: Similarities and Divergent Patterns in Simulated River Discharge S. Xu et al.
305. Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics F. Clerc-Schwarzenbach & T. do Nascimento
306. Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow B. Gordon et al.
307. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al.
308. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al.
309. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al.
310. An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM F. Hu et al.
311. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al.
312. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al.
313. Understanding the relationship between streamflow forecast skill and value across the western US P. Modi et al.
314. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell
315. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli
316. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert
317. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al.
318. BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada D. Kovacek & S. Weijs
319. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt
320. Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales G. Abeshu et al.
321. Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable M. Ibrahim et al.
322. Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al.
323. Residual dynamics in hydrological models: insights from a large sample of catchments and models L. Lombardo et al.
324. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al.
325. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al.
326. Spatial Delimitation of Small Headwater Catchments and Their Classification in Terms of Runoff Risks P. Kavka
327. Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets G. Papacharalampous & H. Tyralis
328. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al.
329. Comprehensive assessment of baseflow responses to long-term meteorological droughts across the United States S. Lee & H. Ajami
330. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al.
331. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al.
332. Mapping (dis)agreement in hydrologic projections L. Melsen et al.
333. Uncertainties in measuring and estimating water‐budget components: Current state of the science S. Levin et al.
334. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al.
335. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al.
336. Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos
337. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al.
338. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al.
339. Analyzing Multi-Year Nitrate Concentration Evolution in Alabama Aquatic Systems Using a Machine Learning Model B. KarimiDermani et al.
340. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni
341. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al.
342. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al.
343. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al.
344. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al.
345. Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature A. Meira Neto et al.
346. Integrated catchment classification across China based on hydroclimatological and geomorphological similarities using self-organizing map and fuzzy c-means clustering for hydrological modeling J. Niu et al.
347. Flood Frequency Modeling and Prediction of Beki and Pagladia Rivers Using Deep Learning Approach G. Devi et al.
348. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al.
349. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al.
350. A benchmark dataset for machine learning in ecotoxicology C. Schür et al.
351. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al.
352. Using R in hydrology: a review of recent developments and future directions L. Slater et al.
353. Hydrological signatures in Brazilian catchments: a machine learning approach for spatial estimates G. de Oliveira Correa et al.
354. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger
355. Climate and rivers G. McGregor
356. Streamflow regime of a lake‐stream system based on long‐term data from a high‐density hydrometric network D. Hudson et al.
357. Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems E. Uncuoglu et al.
358. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
359. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al.
360. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al.
361. GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features M. Ohmer et al.
362. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al.
363. Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison K. Sink & T. Brikowski
364. A regional comparison of sub-daily flow variability in regulated and unregulated rivers in the United States B. Bozeman et al.
365. Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al.
366. Assessing the transferability of LSTM-based streamflow models under varying source basin diversity and target data availability (Mangla Basin, Pakistan) M. Adnan et al.
367. Solute export patterns across the contiguous USA D. Kincaid et al.
368. An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada
369. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al.
370. ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al.
371. Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities S. Gnann et al.
372. Importance of Reconstructed Daily Peak Flows for Enhanced Flood Frequency Analysis: An Application in the Contiguous United States Y. Bandara & G. Mascaro
373. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al.
374. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al.
375. A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models C. Nai et al.
376. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo
377. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan
378. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al.
379. Direct vegetation response to recent CO2 rise shows limited effect on global streamflow H. Wei et al.
380. Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting Y. Tian et al.
381. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al.
382. Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed Q. Fang et al.
383. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous
384. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al.
385. On the comparability between studies in predictive ecotoxicology C. Schür et al.
386. Hybrid modeling for daily streamflow forecasting: A study over the contiguous United States F. Zeng et al.
387. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al.
388. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al.
389. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al.
390. Zero-shot forecasting of streamflow using time series foundation models: are we there yet? A. Sun & A. Sun
391. Seasonality and Drivers of Low Flows Across Europe and the United States M. Floriancic et al.
392. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data S. Goeking & D. Tarboton
393. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al.
394. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al.
395. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski
396. How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies? T. do Nascimento et al.
397. The importance of source data in river network connectivity modeling: A review C. Brinkerhoff
398. DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology A. Adombi
399. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al.
400. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso
401. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton
402. QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification A. Kapoor & R. Chandra
403. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse
404. Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation P. Li et al.
405. Artificial Intelligence (AI) in Surface Water Management: A Comprehensive Review of Methods, Applications, and Challenges J. Gacu et al.
406. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al.
407. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al.
408. Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al.
409. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al.
410. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al.
411. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al.
412. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al.
413. Toward an improved estimation of flood frequency statistics from simulated flows L. Hu et al.
414. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
415. Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al.
416. Remote Sensing of Water: The Observation-to-Inference Arc Across Six Decades and Toward an AI-Native Future D. Ames
417. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al.
418. Increasing importance of temperature as a contributor to the spatial extent of streamflow drought M. Brunner et al.
419. Embracing large language model (LLM) technologies in hydrology research Z. Ma et al.
420. Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al.
421. A two-stage partitioning monthly model and assessment of its performance on runoff modeling C. Deng & W. Wang
422. Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling B. Ghanati & J. Serra-Sagristà
423. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al.
424. Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests M. Yang & F. Olivera
425. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al.
426. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
427. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al.
428. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al.
429. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al.
430. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
431. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al.
432. Streamflow elasticity as a function of aridity V. Andréassian et al.
433. Getting your money's worth: Testing the value of data for hydrological model calibration J. Seibert et al.
434. Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning S. Kim et al.
435. Vegetation optimality explains the convergence of catchments on the Budyko curve R. Nijzink & S. Schymanski
436. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al.
437. An autoencoder-based snow drought index S. Rasiya Koya et al.
438. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al.
439. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al.
440. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al.
441. Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins M. Dal Molin et al.
442. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al.
443. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
444. On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al.
445. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al.
446. Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms Y. You et al.
447. Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning Y. Fang et al.
448. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al.
449. CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia K. Fowler et al.
450. GRQA: Global River Water Quality Archive H. Virro et al.
451. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher
452. Decreases in Mean Annual Streamflow and Interannual Streamflow Variability Across Snow‐Affected Catchments Under a Warming Climate Z. Liu et al.
453. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al.
454. China’s nationwide streamflow decline driven by landscape changes and human interventions K. Wang et al.
455. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al.
456. Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow K. Leeming et al.
457. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
458. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al.
459. Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition Y. Jia et al.
460. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al.
461. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al.
462. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al.
463. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al.
464. Meteorological and hydrological dry-to-wet transition events are only weakly related over European catchments M. Brunner et al.
465. DBv2: an improved climate-centric calibration-free model for runoff-generation simulation P. Istalkar & B. Biswal
466. A conceptual model framework for integrating monitored-unmonitored and surface-subsurface flow contributions to the Baltic Sea A. Vijayan et al.
467. Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales H. Fazekas et al.
468. The general formulation for mean annual runoff components estimation and their change attribution Y. He et al.
469. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al.
470. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al.
471. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al.
472. Enhancing runoff prediction accuracy of deep learning model using baseflow separation method and timestamp information C. Jin et al.
473. smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al.
474. An integrated intelligent model for simulating and optimizing regional water resource sustainability under multiple pressures S. Li & Q. Pei
475. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al.
476. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al.
477. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al.
478. Advancing the science of headwater streamflow for global water protection H. Golden et al.
479. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al.
480. Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States A. Van Beusekom et al.
481. Spatiotemporal evolution and driving mechanisms of compound dry and hot events in the Yellow River Basin under climate warming X. Feng et al.
482. Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States H. Yan et al.
483. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al.
484. Terrestrial ecosystems enhanced root zone water storage capacity in response to climate change over the past four decades Q. Xi et al.
485. Scientific machine learning in hydrology: a unified perspective A. Adombi
486. In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al.
487. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al.
488. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al.
489. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al.
490. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al.
491. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al.
492. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al.
493. A water-energy complementary model for monthly runoff simulation Y. Zou et al.
494. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al.
495. Earthquake Nowcasting with Deep Learning G. Fox et al.
496. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous
497. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al.
498. Is There a Baseflow Budyko Curve? S. Gnann et al.
499. EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al.
500. Accelerating flood warnings by 10 hours: the power of river network topology in AI-enhanced flood forecasting H. Wang et al.
501. Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al.
502. Deep learning foundation and pattern models: Challenges in hydrological time series J. He et al.
503. Expectile-based hydrological modelling for uncertainty estimation: Life after mean H. Tyralis et al.
504. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al.
505. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al.
506. HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al.
507. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al.
508. Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al.
509. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
510. Comparative analysis of machine learning based dissolved oxygen predictions in the Yellow River Basin: The role of diverse environmental predictors L. Liu et al.
511. Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters T. Yoshida et al.
512. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al.
513. A Community Dataset for Large-Scale River Nitrogen Modeling in the United States S. Chang et al.
514. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al.
515. Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest S. Jain et al.
516. Enhancing hydrological prediction in snow-dominant basins through multivariate calibration-assimilation framework F. Aderyani et al.
517. Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns